We tackle the problem of cross-domain few-shot learning where there is a large shift between the base and target domain. We propose a simple solution to utilize unlabeled images from the novel/base dataset by calculating pseudo soft-label from the weakly-augmented version of the unlabeled image and compare it with the strongly augmented version. Our model outperforms the current state-of-the art method by 2.7% for 5-shot and 3.6% for 1-shot classification in the BSCD-FSL benchmark.